How to Analyze Your Process: A Practical Guide to Process Mining Insights

The Gap Between Data and Insights

”Just use process mining and you’ll get insights!” That’s what you often hear. But here’s the truth: insights don’t come out of thin air. While modern process mining tools like ProcessMind automatically discover your processes and calculate metrics, turning those numbers into actionable improvements requires effort and skill.

Yes, AI-powered recommendations can give you some insights for free that previously required hours of manual work. ProcessMind’s AI recommendations surface potential bottlenecks and improvement opportunities automatically. But even with AI assistance, you still need to steer the analysis in the right direction.

Some people analyze data naturally. Others don’t know where to start. Whether you’re in the first or second camp, this guide will help you get better insights from your process mining data. We’ll focus specifically on the analysis phase: you have dashboards, now what do you do with them?

This blog is part of our process improvement series. See also our guides on implementing improvements and continuous monitoring for the complete improvement cycle.

Step 2: Explore the Data

Now that you understand your dashboards, it’s time to explore. In this phase, you’re not looking for anything specific. You’re getting familiar with the data and discovering what’s interesting.

Start with Process Animations

Process animations are the fastest way to understand how your process really flows:

• Watch how cases move through the process
• Notice where traffic jams occur (bottlenecks)
• Spot loops where cases go back and forth (rework)
• See which paths are highways versus backroads

Let the animation run for a few minutes. Patterns will emerge that you might miss in static charts.

Play with Filters

Use filters to slice the data different ways:

• Time periods: How does this month compare to last quarter?
• Case attributes: Do different customer types behave differently?
• Outcomes: What’s different about successful vs. problematic cases?

Every filter change shows you something new about your process.

Explore Dimensions

Use selectors to analyze by different dimensions:

• Departments or teams
• Regions or locations
• Product types or service categories
• Customer segments

You might discover that what looks like a single process is actually several different processes operating under one name.

Examine Process Variants

Every case takes a path through your process. Process variants show you all the unique paths and how often they occur:

• What’s the “happy path” that most cases follow?
• What are the common deviations?
• How do variant performance metrics compare?

Often, a small number of variants account for most of your cases, while dozens of rare variants represent exceptions and edge cases.

Tell Yourself Stories

As you explore, try to explain what you’re seeing. Create narratives about why certain patterns exist:

• “Orders from this region take longer because they require additional approval"
• "Cases with this characteristic often get stuck in this queue"
• "When this happens, we see this consequence”

These stories help you remember patterns and form hypotheses for deeper analysis.

Step 4: Deep Dive Analysis

Now comes the detailed work. For each priority question, systematically investigate using your process mining tools.

Match Questions to Dashboards

Different questions need different analytical tools:

Drill Down Systematically

For each question:

1. Start broad - Look at the overall picture on the relevant dashboard
2. Apply filters - Isolate the cases you’re interested in
3. Compare segments - What’s different between good and bad performers?
4. Examine outliers - Use the case explorer for extreme examples
5. Validate patterns - Check if what you’re seeing is consistent

Document Your Findings

As you discover insights:

• Take screenshots of key visualizations
• Note the filters and settings used
• Write down your interpretation
• Capture potential business impact

You can export charts directly from ProcessMind for use in presentations.

Fix Critical Data Issues

At this stage, if data quality is blocking your analysis (not just imperfect), address it. But be selective: fix only what’s preventing answers to your priority questions.

Common Analysis Techniques

Here are specific analysis techniques you can apply to answer common process questions. Each technique uses different ProcessMind features to uncover insights.

Cycle Time Analysis

Goal: Understand how long cases take and where time is spent.

Tools: Process graph with time metrics, time distribution charts

How to do it:

1. Switch the process graph metric to Average Throughput Time or Average Processing Time
2. Look at the numbers on each connection to see where cases spend the most time
3. Identify the longest transitions (thickest/darkest arrows when showing time)
4. Distinguish between processing time (active work) and wait time (idle time between steps)

What to look for:

• Connections with unexpectedly high time values
• Activities where cases “rest” before continuing
• Differences between median and average (indicates outliers)
• Time patterns by case type, region, or other dimensions

Key insight: Long cycle times often come from wait time, not processing time. A 5-day process might have only 2 hours of actual work.

Bottleneck Analysis

Goal: Find where cases get stuck or delayed in your process.

Tools: Process animation, process graph, time per activity charts

How to do it:

1. Process Animation: Watch the animation and look for activities where dots accumulate. These “traffic jams” indicate bottlenecks where cases wait.

2. Process Graph: Switch to time metrics and find connections with the longest durations. High time on an incoming connection often means cases queue before that activity.

3. Bar Charts: Look at the “Time per Activity” chart to see which activities consume the most time overall.

What to look for:

• Clusters of cases building up at specific activities
• Activities with high average time but low processing time (indicating wait time)
• Steps that feed into bottlenecks (they may complete quickly but cases wait afterward)
• Time variation: some cases fast, others extremely slow

Key insight: Bottlenecks often occur before the slow activity, not at it. Cases might complete a step quickly but then wait in a queue for the next step.

Rework Analysis

Goal: Find where cases loop back to repeat steps unnecessarily.

Tools: Process graph, process animation, variant analysis

How to do it:

1. Look for backward arrows in the process graph (connections going from later to earlier activities)
2. Watch the process animation with Show Tail enabled to see cases moving against the normal flow
3. Filter to cases that include repeated activities using the filter panel
4. Check variant analysis for paths that include the same activity multiple times

What to look for:

• Activities that cases visit more than once
• Loops between specific activities (e.g., “Review” → “Correct” → “Review”)
• The percentage of cases that experience rework
• How much extra time rework adds to the process

Key insight: Some rework is expected (quality checks, corrections), but excessive rework often indicates unclear requirements, quality issues, or communication problems.

Conformance Analysis

Goal: Compare what actually happens to what should happen.

Tools: Process graph, variant analysis, filters

How to do it:

1. Define the “expected” path through your process (the happy path)
2. Use the process graph to see what percentage of cases follow that path
3. Filter to cases that deviate and investigate why
4. Use variant analysis to see all the different paths cases take

What to look for:

• Activities that should always happen but sometimes get skipped
• Activities that happen but shouldn’t (workarounds, manual fixes)
• Unexpected paths between activities
• Cases that end prematurely (don’t reach the end event)

Key insight: Deviations aren’t always bad. Sometimes workarounds indicate a better way to work that should become the new standard.

Resource Analysis

Goal: Understand who does what and how workload is distributed.

Tools: Selectors, filters, resource charts

How to do it:

1. Use selectors to break down the process by resource, team, or department
2. Compare cycle times and throughput across different resources
3. Look for imbalances in workload distribution
4. Identify activities where specific resources or teams are involved

What to look for:

• Uneven distribution of work across resources
• Resources that are consistently slower or faster than others
• Handoff patterns between teams or departments
• Activities that require specific expertise (potential single points of failure)

Key insight: Performance differences between resources might indicate training needs, tool issues, or process design problems rather than individual capability.

Volume and Trend Analysis

Goal: Understand patterns in case volume over time.

Tools: Time filters, trend charts, period comparison

How to do it:

1. Use period filters to compare different time ranges
2. Look at volume trends (are case counts increasing, decreasing, or stable?)
3. Compare the same period across different years or quarters
4. Correlate volume changes with process performance changes

What to look for:

• Seasonal patterns (busy periods, quiet periods)
• Trends that correlate with business events or changes
• How volume changes affect cycle time and quality
• Leading indicators that predict future volume

Key insight: Performance problems might be caused by volume spikes, not process issues. Understanding volume patterns helps you plan capacity.

Path Analysis

Goal: Understand the different routes cases take through your process.

Tools: Process graph, variant analysis, path filters

How to do it:

1. Look at the process graph to identify major decision points (splits)
2. Check the percentages on each outgoing connection to understand routing
3. Use variant analysis to see all unique paths and their frequencies
4. Filter to specific paths to compare their performance

What to look for:

• Dominant paths vs. edge cases
• Performance differences between paths
• Paths that are unexpectedly long or complex
• Opportunities to route cases more efficiently

Key insight: Often 80% of cases follow just a few paths, while dozens of variants account for the remaining 20%. Focus your improvement efforts on the high-volume paths first.

Step 6: What Comes Next

Your analysis is complete and you’ve presented findings. Now what?

Follow Up on Action Items

After your presentation, there will likely be:

• Requests for additional analysis
• Decisions about which improvements to pursue
• Assignments for who will drive changes

Track these and ensure they don’t get lost in day-to-day operations.

Connect to Implementation

Analysis insights need to become actual process changes. See our guide on implementing process optimization for the practical steps of turning insights into improvements.

Set Up Monitoring

Once changes are implemented, you need to verify they’re working. Our guide on continuous process monitoring explains how to track improvements over time.

Document for the Future

Create documentation so your work can be built upon:

• What questions were investigated
• What data was used (and its limitations)
• What findings were discovered
• What decisions were made

This institutional knowledge is valuable for future analysis efforts.

Celebrate Progress

Getting real insights from process data is an accomplishment. Whether you confirmed suspicions, discovered surprises, or identified improvement opportunities, you’ve added value to your organization.

Now start the cycle again. Process improvement is continuous, and there’s always more to learn from your data.

Get Started with ProcessMind

Ready to analyze your own processes? ProcessMind makes process mining accessible with:

• Intuitive dashboards that surface insights quickly
• AI recommendations that highlight potential issues automatically
• Interactive case explorer for deep-dive investigation
• Easy data import from Excel, CSV, or databases

Start your free trial and discover what’s really happening in your processes.

Related Resources:

• What is Process Mining? - Fundamentals guide
• A Strategic Guide for Data-Driven Process Improvement - DMAIC framework
• Process Mining Challenges and Best Practices - Avoiding common pitfalls